With an entity instance, identity verification using verification search generally involves verifying an identity of an individual by collecting and comparing a biometric feature vector (e.g., fingerprint, facial features, etc.) against a template. The template is for example created during a feature extraction phase in which sample features are acquired from a base sample. If the collected biometric features match the template, then the individual is verified. In contrast to verification searching, identity based searching involves comparing collected sets of features to a database of templates to determine an identity of the individual.
Public and private entities, including the FBI, intelligence agencies, and the Department of Defense, are moving towards identity based concepts with respect to verification searches. For example, an investigator might have five high priority cases for which continual monitoring for specific matches is required. In this situation, the investigator would have to run a potentially large identification search on a gallery of data, which can be very time-consuming. One approach therefore is to run a one-to-one verification on a specific image or template. However, if the associated case has a large number of samples, e.g., pictures, the one-to-one verification based on one template might not be an accurate model.